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Sinequa 2017 Predictions: How Big Data, Machine Learning and Artificial Intelligence will Define 2017 and Beyond

VMblog Predictions 2017

Virtualization and Cloud executives share their predictions for 2017.  Read them in this 9th annual series exclusive.

Contributed by Jeff Evernham, Sinequa

How Big Data, Machine Learning and Artificial Intelligence will Define 2017 and Beyond

It's the end of the year again, a time to reflect on the events of 2016 and to predict what lies ahead in 2017. There has been a great deal of buzz around big data, machine learning (ML) and artificial intelligence (AI) this year. However, it's difficult to sort through what's hype and what's not, to determine where these will actually take us in 2017. While we know the trends will continue in some form, what will be new or different next year? It's an exciting time, to be sure, and I am confident that 2017 will not just carry forward the events of 2016, but will have a few new developments as well. Here are some of my predictions:

Incorporation of Unstructured Data

Large companies and organizations are already using big data, but the majority of this data is still structured data (transactions, clicks, etc.). In 2017 we will continue to see a surge in big data usage - but with a much greater focus on leveraging unstructured data. In 2016 much of the use of unstructured data was limited to digital companies whose businesses rely on it. This ability is expanding to enterprises that aren't as data reliant, as more of this data becomes available along with new tools that can manage it without huge investments and long development cycles. In 2017 more organizations will be tapping into their vast stores of disparate, unstructured data by finding ways to leverage it - both by itself and by combining it with their structured data assets to reveal insights not possible with structured data alone.

This will be a fundamental shift. Numbers are typically about revenue performance, operational metrics, etc., but unstructured text holds the critical information about how business actually gets done. When a customer buys something, that transaction tells you about the event - but an e-mail, a transcribed phone call or a Facebook post from that same customer gives you insight about the person.  A company's institutional knowledge (or secret sauce), discoveries, internal processes and competitive edge are often contained in a vast array of written text. It takes natural language processing, unified information access and cognitive search capabilities to extract information and share it in a useful way with those who need it. This will allow organizations to understand what the text is saying and use that to drive innovation and efficiency as well as improve operational effectiveness.

Machine Learning Becomes Legit, but Not Mainstream in 2017

There has been a lot of hype around machine learning for some time now. Over the past couple of decades, we've seen hype around machine intelligence, starting with expert systems and AI, that in most cases didn't deliver. That has changed with the confluence of large data, cheap storage, massive processing power and advanced algorithms - and now machine learning is a reality. Nonetheless, there's still a lot of hype; and while ML will gain adoption for more real-world, successful applications in 2017 it will not become mainstream. This is because the alignment of proper data, suitable training sets and specific use cases - necessary for successful ML - remains hard to come by. ML won't become mainstream until we have more context-aware algorithms and more robust datasets. However, next year will see more ML successes, and that will propel it from a mystical, over-hyped holy grail to having a credible place in the business toolkit.

One area where machine learning is growing rapidly and already showing success is for cognitive search and analytics applications. It won't take over core algorithms anytime soon, but ML is already supplementing and enhancing search results based on user actions and smart analysis of content.

Artificial Intelligence Expands Capabilities, but Impacts the Workforce  

Artificial Intelligence is taking the industry by storm, and not just in "Westworld." We're entering a new phase of AI thanks to advances in computing power and volume of data. This has opened the door to solve computational problems on a scale that no human mind could approach - even in a lifetime. The result is that computers are now able to provide responses that aren't dictated by a collection of "if A, then B" rules, offering results that can only be explained by saying that the computer "understands." The benefit is that complex and time-consuming cognitive processes can now be automated, and we can do things at scale that were previously impossible because unlike humans, computers are not overwhelmed by volume. 

We're definitely headed in the direction of workforce displacement and I believe it's going to happen quickly, as there are huge economic incentives to increase efficiency and to automate manual tasks. This will happen faster than we expect because we think linearly, while technology is advancing exponentially. We struggle with that perspective because it quickly outpaces what we can readily grasp, whether that be in size or speed, or both. This will bring additional challenges because the disruption will occur across the occupational spectrum (unlike the industrial revolution, which primarily impacted "low-skill" jobs). I don't see any particular sector being hit by this tidal wave in 2017, but AI is a disruptor like we've never seen before and it will be here soon whether we are ready for it or not. 

However, with this transformation, tasks that have been impractical because of the time/labor involved now become feasible, which means we'll be able to do things we haven't been able to do before. It will also free us from many mundane and repetitive tasks, enabling people to focus on new or more valuable activities. This will increase efficiency in the workplace as well as consistency, which will improve quality and safety. So while the workforce will look very different from how it looks today - certainly in 10 years and probably in five, AI and ML are going to greatly extend and expand our capabilities in ways that, for now, we can only imagine.

What are your predictions for 2017 and beyond?


About the Author

Jeff Evernham is the Director of Consulting for North America at Sinequa, where he leads Sinequa's expansion in North America with responsibility for client engagements, sales engineering, solution delivery, and partner management. He specializes in aligning cutting-edge technology solutions with business needs, with over twenty years of experience in software, professional services, and management consulting. Jeff has deep expertise in data analysis and business intelligence, and led the analytics and visualization practice at Knowledgent, a big data and analytics consulting firm. He was instrumental in the rapid growth of Synygy, a software and services provider, where he served for over 15 years, attaining the role of Vice President of Global Professional Services. He began his career as a Technical Specialist at The Boeing Company after graduating with Bachelor and Master of Science degrees in Aerospace Engineering from MIT.
Published Tuesday, January 03, 2017 9:02 AM by David Marshall
Artificial Intelligence in 2017: Expands Capabilities, but Impacts the Workforce | Cognitive Search & Analytics - (Author's Link) - January 5, 2017 8:47 AM
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